code: def render_parallel(original_img, net_g, meta_brushes):
      patch_size = 32
      stroke_num = 8
  
      with paddle.no_grad():
          original_h, original_w = original_img.shape[-2:]
          K = max(
              math.ceil(
                  math.log2(max(original_h, original_w) / patch_size)
              ),
              0,
          )
          original_img_pad_size = patch_size * (2**K)
          original_img_pad = render_utils.pad(
              original_img, original_img_pad_size, original_img_pad_size
          )
          final_result = paddle.zeros_like(original_img)
  
          for layer in range(0, K + 1):
              layer_size = patch_size * (2**layer)
  
              img = F.interpolate(
                  original_img_pad, (layer_size, layer_size)
              )
              result = F.interpolate(
                  final_result, (layer_size, layer_size)
              )
              img_patch = F.unfold(
                  img,
                  [patch_size, patch_size],
                  strides=[patch_size, patch_size],
              )
              result_patch = F.unfold(
                  result,
                  [patch_size, patch_size],
                  strides=[patch_size, patch_size],
              )
  
              # There are patch_num * patch_num patches in total
              patch_num = (layer_size - patch_size) // patch_size + 1
              param, decision = stroke_net_predict(
                  img_patch,
                  result_patch,
                  patch_size,
                  net_g,
                  stroke_num,
                  patch_num,
              )
  
              # print(param.shape, decision.shape)
              final_result = param2img_parallel(
                  param, decision, meta_brushes, final_result
              )
  
          # paint another time for last layer
          border_size = original_img_pad_size // (2 * patch_num)
          img = F.interpolate(
              original_img_pad, (layer_size, layer_size)
          )
          result = F.interpolate(final_result, (layer_size, layer_size))
          img = F.pad(
              img,
              [
                  patch_size // 2,
                  patch_size // 2,
                  patch_size // 2,
                  patch_size // 2,
              ],
          )
          result = F.pad(
              result,
              [
                  patch_size // 2,
                  patch_size // 2,
                  patch_size // 2,
                  patch_size // 2,
              ],
          )
          img_patch = F.unfold(
              img,
              [patch_size, patch_size],
              strides=[patch_size, patch_size],
          )
          result_patch = F.unfold(
              result,
              [patch_size, patch_size],
              strides=[patch_size, patch_size],
          )
          final_result = F.pad(
              final_result,
              [border_size, border_size, border_size, border_size],
          )
          patch_num = (img.shape[2] - patch_size) // patch_size + 1
          # w = (img.shape[3] - patch_size) // patch_size + 1
  
          param, decision = stroke_net_predict(
              img_patch,
              result_patch,
              patch_size,
              net_g,
              stroke_num,
              patch_num,
          )
  
          final_result = param2img_parallel(
              param, decision, meta_brushes, final_result
          )
  
          final_result = final_result[
              :, :, border_size:-border_size, border_size:-border_size
          ]
          final_result = (
              final_result.numpy()
              .squeeze()
              .transpose([1, 2, 0])[:, :, ::-1]
              * 255
          ).astype(np.uint8)
          return final_result
  
file_path: PaddleHub/modules/image/Image_gan/style_transfer/paint_transformer/render_parallel.py
imports: import render_utils
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import numpy
  import math
label_desc: Use image node_id 4 for calling a global function with example usage: render_parallel(original_img, net_g, meta_brushes) and returns: final_result